
'''simple docstring'''






import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


UpperCAmelCase_							: Optional[int] =							{
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}







def 		A_							(						_lowerCAmelCase :     Any				):



      """simple docstring"""




      assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
      assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
      if args.mlm:
            assert os.path.isfile(args.token_counts				)
            assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
      else:
            assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

      assert args.teacher_type == args.student_type or (
          args.student_type == "distilbert" and args.teacher_type == "bert"
      )
      assert os.path.isfile(args.student_config				)
      if args.student_pretrained_weights is not None:
            assert os.path.isfile(args.student_pretrained_weights				)

      if args.freeze_token_type_embds:
            assert args.student_type in ["roberta"]

      assert args.alpha_ce >= 0.0
      assert args.alpha_mlm >= 0.0
      assert args.alpha_clm >= 0.0
      assert args.alpha_mse >= 0.0
      assert args.alpha_cos >= 0.0
      assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0







def 		A_							(						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     str				):



      """simple docstring"""




      if args.student_type == "roberta":
            _lowerCamelCase					: Optional[Any]		    = False
      elif args.student_type == "gpt2":
            _lowerCamelCase					: Optional[int]		    = False







def 		A_							(						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     Any				):



      """simple docstring"""




      if args.student_type == "roberta":
            _lowerCamelCase					: Dict		    = False







def 		A_							(						):



      """simple docstring"""




      _lowerCamelCase					: Tuple		    = argparse.ArgumentParser(description="Training"				)
      parser.add_argument("--force"					,						action="store_true"					,						help="Overwrite dump_path if it already exists."				)

      parser.add_argument(
          "--dump_path"					,						type=UpperCamelCase__					,						required=UpperCamelCase__					,						help="The output directory (log, checkpoints, parameters, etc.)"				)
      parser.add_argument(
          "--data_file"					,						type=UpperCamelCase__					,						required=UpperCamelCase__					,						help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."					,						)

      parser.add_argument(
          "--student_type"					,						type=UpperCamelCase__					,						choices=["distilbert", "roberta", "gpt2"]					,						required=UpperCamelCase__					,						help="The student type (DistilBERT, RoBERTa)."					,						)
      parser.add_argument("--student_config"					,						type=UpperCamelCase__					,						required=UpperCamelCase__					,						help="Path to the student configuration."				)
      parser.add_argument(
          "--student_pretrained_weights"					,						default=UpperCamelCase__					,						type=UpperCamelCase__					,						help="Load student initialization checkpoint."				)

      parser.add_argument(
          "--teacher_type"					,						choices=["bert", "roberta", "gpt2"]					,						required=UpperCamelCase__					,						help="Teacher type (BERT, RoBERTa)."				)
      parser.add_argument("--teacher_name"					,						type=UpperCamelCase__					,						required=UpperCamelCase__					,						help="The teacher model."				)

      parser.add_argument("--temperature"					,						default=2.0					,						type=UpperCamelCase__					,						help="Temperature for the softmax temperature."				)
      parser.add_argument(
          "--alpha_ce"					,						default=0.5					,						type=UpperCamelCase__					,						help="Linear weight for the distillation loss. Must be >=0."				)
      parser.add_argument(
          "--alpha_mlm"					,						default=0.0					,						type=UpperCamelCase__					,						help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."					,						)
      parser.add_argument("--alpha_clm"					,						default=0.5					,						type=UpperCamelCase__					,						help="Linear weight for the CLM loss. Must be >=0."				)
      parser.add_argument("--alpha_mse"					,						default=0.0					,						type=UpperCamelCase__					,						help="Linear weight of the MSE loss. Must be >=0."				)
      parser.add_argument(
          "--alpha_cos"					,						default=0.0					,						type=UpperCamelCase__					,						help="Linear weight of the cosine embedding loss. Must be >=0."				)

      parser.add_argument(
          "--mlm"					,						action="store_true"					,						help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."				)
      parser.add_argument(
          "--mlm_mask_prop"					,						default=0.1_5					,						type=UpperCamelCase__					,						help="Proportion of tokens for which we need to make a prediction."					,						)
      parser.add_argument("--word_mask"					,						default=0.8					,						type=UpperCamelCase__					,						help="Proportion of tokens to mask out."				)
      parser.add_argument("--word_keep"					,						default=0.1					,						type=UpperCamelCase__					,						help="Proportion of tokens to keep."				)
      parser.add_argument("--word_rand"					,						default=0.1					,						type=UpperCamelCase__					,						help="Proportion of tokens to randomly replace."				)
      parser.add_argument(
          "--mlm_smoothing"					,						default=0.7					,						type=UpperCamelCase__					,						help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."					,						)
      parser.add_argument("--token_counts"					,						type=UpperCamelCase__					,						help="The token counts in the data_file for MLM."				)

      parser.add_argument(
          "--restrict_ce_to_mask"					,						action="store_true"					,						help="If true, compute the distillation loss only the [MLM] prediction distribution."					,						)
      parser.add_argument(
          "--freeze_pos_embs"					,						action="store_true"					,						help="Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only."					,						)
      parser.add_argument(
          "--freeze_token_type_embds"					,						action="store_true"					,						help="Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only."					,						)

      parser.add_argument("--n_epoch"					,						type=UpperCamelCase__					,						default=3					,						help="Number of pass on the whole dataset."				)
      parser.add_argument("--batch_size"					,						type=UpperCamelCase__					,						default=5					,						help="Batch size (for each process)."				)
      parser.add_argument(
          "--group_by_size"					,						action="store_false"					,						help="If true, group sequences that have similar length into the same batch. Default is true."					,						)

      parser.add_argument(
          "--gradient_accumulation_steps"					,						type=UpperCamelCase__					,						default=50					,						help="Gradient accumulation for larger training batches."					,						)
      parser.add_argument("--warmup_prop"					,						default=0.0_5					,						type=UpperCamelCase__					,						help="Linear warmup proportion."				)
      parser.add_argument("--weight_decay"					,						default=0.0					,						type=UpperCamelCase__					,						help="Weight decay if we apply some."				)
      parser.add_argument("--learning_rate"					,						default=5E-4					,						type=UpperCamelCase__					,						help="The initial learning rate for Adam."				)
      parser.add_argument("--adam_epsilon"					,						default=1E-6					,						type=UpperCamelCase__					,						help="Epsilon for Adam optimizer."				)
      parser.add_argument("--max_grad_norm"					,						default=5.0					,						type=UpperCamelCase__					,						help="Max gradient norm."				)
      parser.add_argument("--initializer_range"					,						default=0.0_2					,						type=UpperCamelCase__					,						help="Random initialization range."				)

      parser.add_argument(
          "--fp16"					,						action="store_true"					,						help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"					,						)
      parser.add_argument(
          "--fp16_opt_level"					,						type=UpperCamelCase__					,						default="O1"					,						help=(
              "For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."
              "See details at https://nvidia.github.io/apex/amp.html"
          )					,						)
      parser.add_argument("--n_gpu"					,						type=UpperCamelCase__					,						default=1					,						help="Number of GPUs in the node."				)
      parser.add_argument("--local_rank"					,						type=UpperCamelCase__					,						default=-1					,						help="Distributed training - Local rank"				)
      parser.add_argument("--seed"					,						type=UpperCamelCase__					,						default=56					,						help="Random seed"				)

      parser.add_argument("--log_interval"					,						type=UpperCamelCase__					,						default=500					,						help="Tensorboard logging interval."				)
      parser.add_argument("--checkpoint_interval"					,						type=UpperCamelCase__					,						default=4000					,						help="Checkpoint interval."				)
      _lowerCamelCase					: Optional[Any]		    = parser.parse_args()
      sanity_checks(UpperCamelCase__				)

      # ARGS #
      init_gpu_params(UpperCamelCase__				)
      set_seed(UpperCamelCase__				)
      if args.is_master:
            if os.path.exists(args.dump_path				):
                  if not args.force:
                        raise ValueError(
                            F'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
                            " itUse `--force` if you want to overwrite it"				)
                  else:
                        shutil.rmtree(args.dump_path				)

            if not os.path.exists(args.dump_path				):
                  os.makedirs(args.dump_path				)
            logger.info(F'Experiment will be dumped and logged in {args.dump_path}'				)

            # SAVE PARAMS #
            logger.info(F'Param: {args}'				)
            with open(os.path.join(args.dump_path					,						"parameters.json"				)					,						"w"				) as f:
                  json.dump(vars(UpperCamelCase__				)					,						UpperCamelCase__					,						indent=4				)
            git_log(args.dump_path				)

      _lowerCamelCase    ,		_lowerCamelCase    ,		_lowerCamelCase					: int		    = MODEL_CLASSES[args.student_type]
      _lowerCamelCase    ,		_lowerCamelCase    ,		_lowerCamelCase					: Union[str, Any]		    = MODEL_CLASSES[args.teacher_type]

      # TOKENIZER #
      _lowerCamelCase					: Tuple		    = teacher_tokenizer_class.from_pretrained(args.teacher_name				)
      _lowerCamelCase					: List[str]		    = {}
      for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
            _lowerCamelCase					: str		    = tokenizer.all_special_tokens.index(UpperCamelCase__				)
            _lowerCamelCase					: Optional[int]		    = tokenizer.all_special_ids[idx]
      logger.info(F'Special tokens {special_tok_ids}'				)
      _lowerCamelCase					: Tuple		    = special_tok_ids
      _lowerCamelCase					: Tuple		    = tokenizer.max_model_input_sizes[args.teacher_name]

      # DATA LOADER #
      logger.info(F'Loading data from {args.data_file}'				)
      with open(args.data_file					,						"rb"				) as fp:
            _lowerCamelCase					: List[Any]		    = pickle.load(UpperCamelCase__				)

      if args.mlm:
            logger.info(F'Loading token counts from {args.token_counts} (already pre-computed)'				)
            with open(args.token_counts					,						"rb"				) as fp:
                  _lowerCamelCase					: List[Any]		    = pickle.load(UpperCamelCase__				)

            _lowerCamelCase					: Union[str, Any]		    = np.maximum(UpperCamelCase__					,						1				) ** -args.mlm_smoothing
            for idx in special_tok_ids.values():
                  _lowerCamelCase					: str		    = 0.0  # do not predict special tokens
            _lowerCamelCase					: Optional[Any]		    = torch.from_numpy(UpperCamelCase__				)
      else:
            _lowerCamelCase					: Optional[int]		    = None

      _lowerCamelCase					: Union[str, Any]		    = LmSeqsDataset(params=UpperCamelCase__					,						data=UpperCamelCase__				)
      logger.info("Data loader created."				)

      # STUDENT #
      logger.info(F'Loading student config from {args.student_config}'				)
      _lowerCamelCase					: Optional[Any]		    = student_config_class.from_pretrained(args.student_config				)
      _lowerCamelCase					: Dict		    = True

      if args.student_pretrained_weights is not None:
            logger.info(F'Loading pretrained weights from {args.student_pretrained_weights}'				)
            _lowerCamelCase					: Union[str, Any]		    = student_model_class.from_pretrained(args.student_pretrained_weights					,						config=UpperCamelCase__				)
      else:
            _lowerCamelCase					: List[str]		    = student_model_class(UpperCamelCase__				)

      if args.n_gpu > 0:
            student.to(F'cuda:{args.local_rank}'				)
      logger.info("Student loaded."				)

      # TEACHER #
      _lowerCamelCase					: Union[str, Any]		    = teacher_model_class.from_pretrained(args.teacher_name					,						output_hidden_states=UpperCamelCase__				)
      if args.n_gpu > 0:
            teacher.to(F'cuda:{args.local_rank}'				)
      logger.info(F'Teacher loaded from {args.teacher_name}.'				)

      # FREEZING #
      if args.freeze_pos_embs:
            freeze_pos_embeddings(UpperCamelCase__					,						UpperCamelCase__				)
      if args.freeze_token_type_embds:
            freeze_token_type_embeddings(UpperCamelCase__					,						UpperCamelCase__				)

      # SANITY CHECKS #
      assert student.config.vocab_size == teacher.config.vocab_size
      assert student.config.hidden_size == teacher.config.hidden_size
      assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
      if args.mlm:
            assert token_probs.size(0				) == stu_architecture_config.vocab_size

      # DISTILLER #
      torch.cuda.empty_cache()
      _lowerCamelCase					: str		    = Distiller(
          params=UpperCamelCase__					,						dataset=UpperCamelCase__					,						token_probs=UpperCamelCase__					,						student=UpperCamelCase__					,						teacher=UpperCamelCase__				)
      distiller.train()
      logger.info("Let\'s go get some drinks."				)


if __name__ == "__main__":
 main()
'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							)      ->       float:
    __lowerCamelCase            =    0
    while len(UpperCamelCase__							) > 1:
        __lowerCamelCase            =    0
        # Consider two files with minimum cost to be merged
        for _ in range(2							):
            __lowerCamelCase            =    files.index(min(UpperCamelCase__							)							)
            temp += files[min_index]
            files.pop(UpperCamelCase__							)
        files.append(UpperCamelCase__							)
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
  import doctest

  doctest.testmod()
